CN112731914A - 一种5g智慧工厂的云化agv应用系统 - Google Patents
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Abstract
一种5G智慧工厂的云化AGV应用系统,即5G智慧工厂云化AGV应用解决方案,云化AGV把AGV上位机运行的定位、导航、图像识别及环境感知等需要复杂计算能力需求的模块上移到5G的边缘计算服务器,以满足AGV日益增长的计算力需求,同时运动控制与紧急避障等实时性要求更高的模块仍然保留在AGV本体以满足安全性等要求。这相当于在云端为AGV增加了一个大脑,除AGV原有的复杂计算以外,各种各样的AI能力扩展成为可能。多台云化AGV可组成柔性生产搬运系统,运行路线可以随着生产工艺流程的调整而及时调整,大大提高了生产的柔性和企业的竞争力。
Description
技术领域
本发明涉及一种AGV应用系统,具体地说涉及一种的一种5G智慧工厂的云化AGV应用系统,是把AGV上位机运行的定位、导航、图像识别及环境感知等需要复杂计算能力需求的模块上移到5G的边缘计算服务器,以满足AGV日益增长的计算力需求,能进行所有的AI能力扩展的一种5G智慧工厂的云化AGV应用系统。
背景技术
现有的4G技术AGV应用系统,是通过Wi-Fi或4G技术将图像信息传输至服务器进行处理,这样压缩后图像的清晰度会受到明显影响,进而影响定位效果,并且无线通信的延迟与不稳定性也会对AGV的正常工作造成影响。
发明内容
本发明的目的是克服现有技术的不足之处,提供一种5G智慧工厂的云化AGV应用系统,是把AGV上位机运行的定位、导航、图像识别及环境感知等需要复杂计算能力需求的模块上移到5G的边缘计算服务器,以满足AGV日益增长的计算力需求,能进行所有的AI能力扩展的一种5G智慧工厂的云化AGV应用系统。
本发明解决其技术问题所采用的技术方案是:一种5G智慧工厂的云化AGV应用系统,包括云化AGV,云化AGV是把AGV上位机运行的定位、导航、图像识别及环境感知等需要复杂计算能力需求的模块上移到5G的边缘计算服务器,以满足AGV日益增长的计算力需求,同时运动控制与紧急避障等实时性要求更高的模块仍然保留在AGV本体以满足安全性的要求,云化AGV相当于在云端为AGV增加了一个大脑,除AGV原有的复杂计算以外,能进行所有的AI能力扩展。云化AGV由边缘计算和云计算组成,边缘计算和云计算的结合可以突破AGV终端的计算能力和存储的限制,提高AI算法的训练和推理能力,同时将大部分机器人智能布署在边缘和云端,通过协作和不断地训练,持续不断地提高AGV智能化程度,多台云化AGV可组成柔性生产搬运系统,运行路线可以随着生产工艺流程的调整进行及时调整,能提高AGV的生产搬运柔性和竞争力。云化AGV通过Wi-Fi或5G技术将图像信息传输至服务器进行处理,图像清晰,不影响定位,能够进行高带宽、低延迟、稳定的5G网络进行数据的传输,完成云化视觉定位,采用激光加视觉的多传感器融合的自主导航方案,视觉定位采用未标记场景的图像信息融合惯性测量单元传感器数据进行全局定位和地图构建的技术(SLAM或VIO),AGV采用视觉实现定位和导航。云化AGV能实现云到边到端的无缝协同计算,云侧提供高性能的计算以及通用知识的存储,边缘侧进行有效的数据处理,提供算力支持,并在边缘范围内实现协同和共享,机器人终端完成实时的操作和处理。
本发明的有益效果是,可以突破AGV终端的计算能力和存储的限制,提高AI算法的训练和推理能力,同时将大部分机器人智能布署在边缘和云端,通过协作和不断地训练,持续不断地提高AGV智能化程度,多台云化AGV可组成柔性生产搬运系统,运行路线可以随着生产工艺流程的调整进行及时调整,能提高AGV的生产搬运柔性和竞争力。云化AGV通过Wi-Fi或5G技术将图像信息传输至服务器进行处理,图像清晰,不影响定位,能够进行高带宽、低延迟、稳定的5G网络进行数据的传输,完成云化视觉定位,采用激光加视觉的多传感器融合的自主导航方案,视觉定位采用未标记场景的图像信息融合惯性测量单元传感器数据进行全局定位和地图构建的技术(SLAM或VIO),实现AGV全方位快速视觉定位和导航。
附图说明
下面结合附图和实施例对本发明进一步说明。
图1是本发明的实施例一的云化AGV架构图。
具体实施方式
实施本发明的一种5G智慧工厂的云化AGV应用系统,包括云化AGV,云化AGV是把AGV上位机运行的定位、导航、图像识别及环境感知等需要复杂计算能力需求的模块上移到5G的边缘计算服务器,以满足AGV日益增长的计算力需求,同时运动控制与紧急避障等实时性要求更高的模块仍然保留在AGV本体以满足安全性的要求,云化AGV相当于在云端为AGV增加了一个大脑,除AGV原有的复杂计算以外,能进行所有的AI能力扩展。
实施本发明的一种5G智慧工厂的云化AGV应用系统,云化AGV由边缘计算和云计算组成,边缘计算和云计算的结合可以突破AGV终端的计算能力和存储的限制,提高AI算法的训练和推理能力,同时将大部分机器人智能布署在边缘和云端,通过协作和不断地训练,持续不断地提高AGV智能化程度,多台云化AGV可组成柔性生产搬运系统,运行路线可以随着生产工艺流程的调整进行及时调整,能提高AGV的生产搬运柔性和竞争力。
实施本发明的一种5G智慧工厂的云化AGV应用系统,云化AGV通过Wi-Fi或5G技术将图像信息传输至服务器进行处理,图像清晰,不影响定位,能够进行高带宽、低延迟、稳定的5G网络进行数据的传输,完成云化视觉定位,采用激光加视觉的多传感器融合的自主导航方案,视觉定位采用未标记场景的图像信息融合惯性测量单元传感器数据进行全局定位和地图构建的技术(SLAM或VIO),AGV采用视觉实现定位和导航。
实施本发明的一种5G智慧工厂的云化AGV应用系统,云化AGV能实现云到边到端的无缝协同计算,云侧提供高性能的计算以及通用知识的存储,边缘侧进行有效的数据处理,提供算力支持,并在边缘范围内实现协同和共享,机器人终端完成实时的操作和处理。
实施本发明的一种5G智慧工厂的云化AGV应用系统,AGV云化控制相当于部署在工业企业内5G网络侧的工业边缘云MEC将AGV作为无线传感器与执行器进行控制,这一控制方式对于数据链路的可靠性以及带宽具有十分苛刻的要求。将AGV的顶层控制器转移至边缘云端后,AGV底层只负责速度与转向控制,通过编码器与惯性测量单元即可实现,顶层图像信息的处理与反馈需要由部署在MEC上的软件实现。在安全避障方面,由于定位不完全依赖于激光数据,AGV可以装备低成本的二维或伪三维激光避障传感器,用于代替昂贵的三维点云激光传感器。
由于AGV的运动学模型相对较为准确,视觉定位算法可以通过相对较快的速度收敛,理论上能够达到较好的精度。如果在室外环境使用,AGV底层还可以携带低成本GPS模块,进一步增加定位的可靠性。MEC通过与先验知识图像信息进行匹配,能够有效地确定所有AGV的全局位置,并根据AGV的状态实时进行自主路径规划和自动避让。
如此,一方面,MEC采用全新的分布式计算方式,构建在用户侧的云服务环境,降低时延和往返时间(RTT),优化流量,增强物理安全和缓存效率等。另一方面,MEC是把终端侧的大量计算和应用需求,从终端侧迁移到MEC边缘侧,实现计算及存储资源的弹性利用,并减少移动业务的端到端时延。
目前机器人本体计算能力有限,必须通过可以无限扩展的云端计算能力来提供智能机器人所需的能力。通过5G无线接入和由安全高速骨干网络构成的机器人的“神经网络”,来实现机器人本体和云端大脑的连接。云端大脑包括机器人视觉系统、对话系统、运动智能和极限现实系统等技术,通过人工智能算法不断训练、进化,使得前端机器人本体智能随之迅速提高。因此,云化AGV的系统架构具有更强的适应性和扩展性。
云化AGV实现了云、边、端的无缝协同计算。云侧提供高性能的计算以及通用知识的存储,边缘侧进行有效的数据处理,提供算力支持,并在边缘范围内实现协同和共享,机器人终端完成实时的操作和处理等基本功能。
云化AGV采用激光导航代替侵入式部署的导航方式。随着场景的日趋复杂,为了弥补2D激光导航在无特征场景和复杂场景应用受限的缺陷,采用了激光+视觉等多传感器融合的自主导航方案。随着视觉定位技术的发展,采用未标记场景的图像信息融合惯性测量单元传感器数据进行全局定位和地图构建的技术(SLAM或VIO)已经较为成熟,AGV采用了更低成本的视觉实现定位和导航。
实施本发明的一种5G智慧工厂的云化AGV应用系统,随着5G和边缘计算的部署,机器人端到基站的延迟可以达到毫秒级,使得5G的网络边缘可以很好地支持AGV的实时应用。通过云化,各AGV本体获取和处理的信息可以保持最新,并安全备份、存储。因此,在通常情况下,云侧可以提供高性能的计算以及通用知识的存储,边缘侧可以更有效地处理数据,提供算力支持,并在边缘范围内实现协同和共享,机器人终端完成实时的操作和处理等基本机器人的功能。
云化AGV把云端大脑分布在从云到端的各个地方,充分利用边缘计算去提供更高性价比的服务,把要完成任务的记忆场景的知识和常识很好地组合起来,实现规模化部署。由于AGV配备了多传感器,在工作过程中可以收集到大量视觉、语音、位置等信息,为了数据安全,建立了安全专网,基于专有路线进行网络传输,确保AGV与云端具有安全的网络连接。
随着5G通信网络商用部署的全面铺开,利用5G技术,可大大缩短从终端到接入网的时间,带宽大幅度上升,很多工业应用可以放到边缘端,加入更多的计算能力,包括云端大脑的一些扩展,助力AGV规模化部署。随着5G网络的规模商用和5G云化AGV在工业制造和仓储物流等领域的大量应用,可大幅提升企业的生产效率和管理水平,节省人力成本。
本技术关键点和保护点列举
云化AGV具有持续学习和协同学习的能力,将感知模块的输出与知识图谱结合对环境和人充分理解,通用知识和较少变化的领域知识存放在云端,与地域和个性化服务相关的知识存放在边缘或者终端,逐步提取和积累与服务场景、个人相关的个性化知识。云化AGV能够在AGV之间或AGV与其他智能体间共享数据、模型、知识库等,进行协同学习
目前AGV(Automated Guided Vehicle,自动导引运输车)技术正被广泛应用于无人物流、仓储以及工业生产过程中。随着视觉定位技术的发展,采用未标记场景的图像信息融合惯性测量单元传感器数据进行全局定位和地图构建的技术(SLAM或VIO)已经较为成熟,这种方法能够使用较低成本的传感器实现AGV的定位与控制,但其所需要的计算资源超出了一般低成本嵌入式计算机所能提供的范围,需要相对高性能的计算机进行处理,因而无法真正有效地降低单台AGV的成本。云化AGV能提高AGV智能化程度。
实施本发明,效果很好。
Claims (4)
1.一种5G智慧工厂的云化AGV应用系统,包括云化AGV,其特征在于,在所述云化AGV是把AGV上位机运行的定位、导航、图像识别及环境感知等需要复杂计算能力需求的模块上移到5G的边缘计算服务器,以满足AGV日益增长的计算力需求,同时运动控制与紧急避障等实时性要求更高的模块仍然保留在AGV本体以满足安全性的要求,云化AGV相当于在云端为AGV增加了一个大脑,除AGV原有的复杂计算以外,能进行所有的AI能力扩展。
2.根据权利要求1所述的一种5G智慧工厂的云化AGV应用系统,其特征在所述云化AGV由边缘计算和云计算组成,边缘计算和云计算的结合可以突破AGV终端的计算能力和存储的限制,提高AI算法的训练和推理能力,同时将大部分机器人智能布署在边缘和云端,通过协作和不断地训练,持续不断地提高AGV智能化程度,多台云化AGV可组成柔性生产搬运系统,运行路线可以随着生产工艺流程的调整进行及时调整,能提高AGV的生产搬运柔性和竞争力。
3.根据权利要求1所述的一种5G智慧工厂的云化AGV应用系统,其特征在所述云化AGV通过Wi-Fi或5G技术将图像信息传输至服务器进行处理,图像清晰,不影响定位,能够进行高带宽、低延迟、稳定的5G网络进行数据的传输,完成云化视觉定位,采用激光加视觉的多传感器融合的自主导航方案,视觉定位采用未标记场景的图像信息融合惯性测量单元传感器数据进行全局定位和地图构建的技术(SLAM或VIO),AGV采用视觉实现定位和导航。
4.根据权利要求1所述的一种5G智慧工厂的云化AGV应用系统,其特征在所述云化AGV能实现云到边到端的无缝协同计算,云侧提供高性能的计算以及通用知识的存储,边缘侧进行有效的数据处理,提供算力支持,并在边缘范围内实现协同和共享,机器人终端完成实时的操作和处理。
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CN113992864A (zh) * | 2021-10-20 | 2022-01-28 | 中国电信股份有限公司 | Agv视觉导航系统、方法、装置、电子设备和介质 |
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CN113934217A (zh) * | 2021-12-15 | 2022-01-14 | 南京绛门信息科技股份有限公司 | 一种基于5g的智能调度处理系统 |
CN113934217B (zh) * | 2021-12-15 | 2022-02-25 | 南京绛门信息科技股份有限公司 | 一种基于5g的智能调度处理系统 |
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